Our method has several advantages:
(1) DSW explicitly takes advantages of the autocorrelation structure
characteristics of sequential data. It provides versatile and flexible
discriminative state representations for sequences;
(2) The alignment results of DSW is semantically more interpretable than plain DTW;
(3) Using one nearest neighbor classifier, DSW exhibits lower error rates
than DTW on most datasets; (4) The flexibility of DSW allows users to fine
tune the representations of sequences to capture the discriminative features
for specific tasks; (5) DSW is able to deal with uni-variate and multivariate
sequences without adjusting the algorithm. (6) DSW shows more robustness to noise and more suitable for long sequences than
compared methods; (7) After obtaining the state sequences, the problem is
again DTW, thus advanced techniques [1]}, [2]} for improving the
effectiveness and efficiency of DTW is also compatible with DSW.
